SegAN | PyTorch implementation of image segmentation GAN | Machine Learning library

by gokriznastic Python Version: Current License: No License

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kandi X-RAY | SegAN Summary

SegAN is a Python library typically used in Healthcare, Pharma, Life Sciences, Artificial Intelligence, Machine Learning, Deep Learning, Pytorch applications. SegAN has no bugs, it has no vulnerabilities and it has low support. However SegAN build file is not available. You can download it from GitHub.

A PyTorch implementation of image segmentation GAN from the paper SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation by Yuan Xue, Tao Xu, Han Zhang, L. Rodney Long, Xiaolei Huang. The data used is from LiTS - Liver Tumor Segmentation Challenge dataset containing abdominal CT scans for liver and tumor segmentation.

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Quality

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Support

SegAN has a low active ecosystem.

It has 15 star(s) with 6 fork(s). There are 2 watchers for this library.

It had no major release in the last 6 months.

There are 1 open issues and 0 have been closed. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of SegAN is current.

Quality

SegAN has 0 bugs and 0 code smells.

Security

SegAN has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

SegAN code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

SegAN does not have a standard license declared.

Check the repository for any license declaration and review the terms closely.

Without a license, all rights are reserved, and you cannot use the library in your applications.

Reuse

SegAN releases are not available. You will need to build from source code and install.

SegAN has no build file. You will be need to create the build yourself to build the component from source.

SegAN saves you 313 person hours of effort in developing the same functionality from scratch.

It has 752 lines of code, 26 functions and 5 files.

It has medium code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed SegAN and discovered the below as its top functions. This is intended to give you an instant insight into SegAN implemented functionality, and help decide if they suit your requirements.

Load image from disk
Convert numpy array to tiff
Create a directory if it does not exist
Compute the dice loss between two input tensors
Merge channels together
Data loader for training images
Make a directory if it exists

Get all kandi verified functions for this library.

SegAN Key Features

No Key Features are available at this moment for SegAN.

SegAN Examples and Code Snippets

No Code Snippets are available at this moment for SegAN.

Community Discussions

Trending Discussions on SegAN

Attempt to call local 'callback' (a nil value) error while trying to train SeGAN model

QUESTION

Attempt to call local 'callback' (a nil value) error while trying to train SeGAN model

Asked 2021-Jan-06 at 11:11

I am trying to implement the "SeGAN: Segmenting and Generating the invisible" paper on ubuntu 18.04 with Geforce RTX 2060. I have installed the Driver, CUDA, cuDNN, Torch7 and dependencies and downloaded and extracted the dataset and weights folders and made a link to them. I tried to train the model with this line of code:

...

ANSWER

Answered 2021-Jan-06 at 11:11

From the linked GitHub repo:

End2EndNetwork.lua line 81

Source https://stackoverflow.com/questions/65594137

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install SegAN

You can download it from GitHub.
You can use SegAN like any standard Python library. You will need to make sure that you have a development environment consisting of a Python distribution including header files, a compiler, pip, and git installed. Make sure that your pip, setuptools, and wheel are up to date. When using pip it is generally recommended to install packages in a virtual environment to avoid changes to the system.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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